Department Digitally Integrated Microstructure and Mechanics
Biaxial testing of a cross sample
The biaxial testing machine can be used to characterise the crack propagation behaviour under complex multi-axial loading. Testing is carried out on a simplified sample geometry of an aircraft fuselage. Specific load-time histories or load spectra can be set axis-specifically. The investigated crack propagation mechanisms can be scaled to component level and thus contribute to the approval and further development of materials, structures or production processes for aircraft fuselage structures.
In order for innovative materials to be used quickly, their behavior in operation must be predicted precisely and reliably. To achieve this, it is important to understand the physical processes that take place in the material under stress in depth and to describe them quantitatively. To this end, we use and develop experimental and numerical methods on all relevant length scales - from the electronic structure, atomic and molecular relationships, the morphology of phases and interfaces to the effects due to the shape and surface properties of components. We focus on materials for high mechanical stresses in a wide temperature range. We accelerate research into new materials with the help of modern artificial intelligence (AI), quantum computing and the automation of our testing and analysis methods.
Understanding Material Behavior - Predicting Component Performance
The mechanical properties of a material are inextricably linked to its internal structure. We investigate this relationship using high-precision theoretical and experimental methods. Our research focuses in particular on fatigue properties and fracture mechanics - essential for the safety of critical components, for example in aviation.
By combining classic experimental technology with cross-scale digital image correlation, robotics and AI, we are setting new standards in experimental mechanics. ‘Self-driving labs’ enable us to automatically generate large data sets in order to uncover previously unknown correlations. These findings are also crucial for the virtual certification of the next generation of aerospace structures - a significant step towards safer, more efficient and more powerful technologies.
Materials Mechanics Testing - Precision Under Extreme Conditions
We characterise high-performance materials under realistic operating conditions. Servo-hydraulic, electromechanical and resonance testing machines with forces from 1 to 1000 kN are available for this purpose, including a biaxial testing machine for cross-shaped test specimens. Our test portfolio includes
Tests under extreme conditions (temperatures from -196°C to 1400°C, corrosive environments)
In our research, we focus on high-temperature materials, e.g. for air jet engines, and on lightweight materials. We work with digital methods such as image correlation, robot-assisted automation and digital workflows to record and analyse test data quickly and precisely. All data is stored sustainably in a data management system in accordance with the FAIR principle (Findable, Accessible, Interoperable, Reusable).
Getting to the Bottom of Materials - Analyses down to the Atomic Scale
Modern materials are characterised by complex microstructures made up of different materials and phases. The basis for understanding material behaviour is the characterisation of these microstructures, from the atomic structure of the phases and interfaces to the classical microstructure and the discontinuities in components resulting from the shape. The Central Analysis and Metallography department prepares the microstructures of the materials and characterises them from the atomic level to the component. A variety of diffraction methods such as X-ray and electron diffraction as well as imaging microscopic methods such as light microscopy and scanning and transmission electron microscopy including the associated spectroscopic methods of energy dispersive X-ray spectroscopy (EDX ) and electron energy loss spectroscopy (EELS) are used. The interpretation of the analysis results is increasingly supported by data-driven evaluation methods and machine learning in order to identify hidden relationships between the material structures and the material properties.
Dual beam, STEM and X-ray powder diffractometer
Dual Beam (FIB) FEI Helios NanoLab600i (0.8 nm resolution) for the preparation of thin lamellae for TEM, equipped for tomography, EDX and WDX (left). ThermoFisher Scientific Spectra 300 (S)TEM with sample corrector (47 pm resolution), with EDX, EELS, 4D STEM, NanoMega's Astar system and tomography add-on (centre). Bruker D8 Advance X-ray powder diffractometer with co-tube and sample changer, one of three X-ray diffractometers (right).
Cross-Scale Modelling - Digital Approaches for new Materials
To accelerate research into new high-performance materials, we rely on state-of-the-art technologies: we use artificial intelligence (AI) to analyse experimental data, combine physical models with in-depth expertise and thus create reliable predictive models. Our goal: to make the development of new materials more efficient, precise and automated.
An in-depth understanding of the fundamental material properties - from electronic structure to mechanical properties - requires simulations on all relevant scales. This is why we develop advanced simulation and optimisation methods using quantum computing, quantum annealing and machine learning.
Core Expertise
Prediction of component performance
Materials mechanics testing and characterisation of high-performance materials
Efficient, precise and automated material development
